565 research outputs found
Iris Recognition Using Scattering Transform and Textural Features
Iris recognition has drawn a lot of attention since the mid-twentieth
century. Among all biometric features, iris is known to possess a rich set of
features. Different features have been used to perform iris recognition in the
past. In this paper, two powerful sets of features are introduced to be used
for iris recognition: scattering transform-based features and textural
features. PCA is also applied on the extracted features to reduce the
dimensionality of the feature vector while preserving most of the information
of its initial value. Minimum distance classifier is used to perform template
matching for each new test sample. The proposed scheme is tested on a
well-known iris database, and showed promising results with the best accuracy
rate of 99.2%
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
Screen Content Image Segmentation Using Sparse-Smooth Decomposition
Sparse decomposition has been extensively used for different applications
including signal compression and denoising and document analysis. In this
paper, sparse decomposition is used for image segmentation. The proposed
algorithm separates the background and foreground using a sparse-smooth
decomposition technique such that the smooth and sparse components correspond
to the background and foreground respectively. This algorithm is tested on
several test images from HEVC test sequences and is shown to have superior
performance over other methods, such as the hierarchical k-means clustering in
DjVu. This segmentation algorithm can also be used for text extraction, video
compression and medical image segmentation.Comment: Asilomar Conference on Signals, Systems and Computers, IEEE, 2015,
(to Appear
Invariant Scattering Transform for Medical Imaging
Invariant scattering transform introduces new area of research that merges
the signal processing with deep learning for computer vision. Nowadays, Deep
Learning algorithms are able to solve a variety of problems in medical sector.
Medical images are used to detect diseases brain cancer or tumor, Alzheimer's
disease, breast cancer, Parkinson's disease and many others. During pandemic
back in 2020, machine learning and deep learning has played a critical role to
detect COVID-19 which included mutation analysis, prediction, diagnosis and
decision making. Medical images like X-ray, MRI known as magnetic resonance
imaging, CT scans are used for detecting diseases. There is another method in
deep learning for medical imaging which is scattering transform. It builds
useful signal representation for image classification. It is a wavelet
technique; which is impactful for medical image classification problems. This
research article discusses scattering transform as the efficient system for
medical image analysis where it's figured by scattering the signal information
implemented in a deep convolutional network. A step by step case study is
manifested at this research work.Comment: 11 pages, 8 figures and 1 tabl
- …